| 书目名称 | Quantum Machine Learning: An Applied Approach | | 副标题 | The Theory and Appli | | 编辑 | Santanu Ganguly | | 视频video | | | 概述 | The first book related to hands-on aspects of quantum machine learning.Optimized for self-study without jargon and centered on easy reading.Code examples utilizing open source libraries and languages | | 图书封面 |  | | 描述 | .Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research..The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost..Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti‘s Forest, D-Wave‘s dOcean, Google‘s Cirq and brand new TensorFlow Quantum, and Xanadu‘s PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares v | | 出版日期 | Book 2021 | | 关键词 | Quantum Mechanics; machine learning; quantum computing; artificial intelligence; Grover’s search algorit | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-7098-1 | | isbn_softcover | 978-1-4842-7097-4 | | isbn_ebook | 978-1-4842-7098-1 | | copyright | Santanu Ganguly 2021 |
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发表于 2025-3-21 19:16:16
